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SCNet

Underwater Image Enhancement via Learning Water Type Desensitized Representations. Accepted by ICASSP 2022. [paper]

You can run eval.py and to obtain the results using our pre-trained model [Baidu Drive(9xwn)] [Google Drive].

You can run get_performance.py to obtian the SSIM, PSNR and LPIPS scores.

To train the model, you need to prepare the dataset first [Baidu Drive(qwat)] [(Google Drive)]. Then, run main.py.

If you find SCNet is useful in your research, please consider citing our paper.

@INPROCEEDINGS{9747758,
  author={Fu, Zhenqi and Lin, Xiaopeng and Wang, Wu and Huang, Yue and Ding, Xinghao},
  booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Underwater Image Enhancement Via Learning Water Type Desensitized Representations}, 
  year={2022},
  pages={2764-2768},
  doi={10.1109/ICASSP43922.2022.9747758}}

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